This is an R Markdown
document. Follow the link to learn more about R Markdown and the
notebook format used during the workshop.
Setup
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
The data is from the Stanford Open Policing
Project and includes vehicle stops by the Evanston police in 2017.
We’re reading the data in from a URL directly.
police <- read_csv("https://raw.githubusercontent.com/nuitrcs/r-tidyverse/main/data/ev_police.csv",
col_types=c( "location"="c"))
dplyr
dplyr is at the core of the tidyverse. It contains six main
functions, each a verb, of actions you frequently take with a data
frame. We covered a few of these in the intro and will build on select,
filter, and mutate in this document.
If you have a background in Excel, you probably will start thinking
about equivalences in your mind. That’s great! If you have worked on
base R (not tidyverse), don’t overthink it. What’s the most important
thing to keep in mind right now? Each of these functions takes a data
frame as the first input. Within the function call, we can refer to the
column names without quotes and without $ notation.
We will begin with the select for choosing columns, and
filter for choosing rows. Remember select is
for columns, while filter is for rows. Let’s start!
Select: Choose Columns
The previous session covered the basics of select but
there are many more options for how we can specify which columns to
choose.
First, let’s remember what the column names are:
names(police)
Recall, the select function takes as the first input a
data frame, and then we can list one or more columns, names unquoted,
that we want to select. The columns will be ordered in the order we
specify them. Notice that the order in the output is different than the
original order above.
police %>% select(outcome, date)
select(police, outcome, date)
Ranges
There are a number of select helper functions and special syntax
options that allow us to choose multiple columns.
First, we can use : for range, but with names in addition to
numbers:
police %>% select(raw_DriverRace:raw_ResultOfStop)
The result is the same if we use numbers.
police %>% select(26:29)
We can select the rightmost columns with last_col(). You
can check about with names that it is the “last” column in
our dataset.
police %>% select(last_col())
You can think everything() as the opposite of selecting
one or a few columns. But it is most useful when combined with other
functions. After all, we use select because you don’t want the whole
database but part of it! We will see the use of
everything() and other selection operators below.
police %>% select(everything())
Excluding columns
We can also say which columns we don’t want by putting a
- (minus) in front of the name. Think about it as
subtracting or or more columns. The idea of subtracting makes a lot of
sense – we have now 27 rather than the original 29 columns in the
data.
police %>% select(-raw_row_number, -subject_age)
When using negated - column names, if we start with
negations, it will get messy. Make sure to avoid the following
code:
police %>% select(-raw_row_number, -subject_age, time:outcome)
To both specify the columns wanted and exclude some that would
otherwise be selected, the exclusions need to come at the end:
police %>% select(time:outcome, -subject_age)
We don’t need to include -raw_row_number – it is not part of the
range between time and outcome. If we do include it, we will still get
the same result.
police %>% select(time:outcome, -subject_age, -raw_row_number)
Reordering and renaming
We’ve already seen that columns will appear in the result in the
order that we list the names.
If you want to pull a few columns over to the left so that they are
the ones that show first when you look at the data you can use the
everything() helper function.
We did this as well in the intro, see if you can write code below to
use select() and everything() to bring outcome to the left from
police.
Exercise:
Each column only gets included once, in the position that it first
appears. So “outcome” becomes the leftmost column above and no longer
appears in it’s original spot.
We can also rename columns while using select(). The
syntax is new_name = old_name.
police %>% select(raw_id=raw_row_number, date, time)
or we can use rename() to only rename, without affecting
which columns are included or their order (all of the columns are kept
in the same order):
police %>% rename(raw_id=raw_row_number)
Remember, this doesn’t change police because we didn’t save the
result. So far, we’ve just been printing the copy of the data frame that
is returned by the function. If we want to change our data frame, we’d
need to save the result back to the police object.We can
also save the changes in another object with a different name such as
police_new.
Exercise:
Assign the results of renaming raw_row_number to raw_id in police to
police_new
police_new <-
We saved the same data with a different name for one column, while
keep all other and their original order. But we can also save only a few
variables. For convenience, we name police_short our new
object.
police_short <- select(police, raw_row_number, date, time)
EXERCISES
Remember: run the cells above to load tidyverse and import the
data.
Using select and/or rename as needed:
- Rename subject_age to age, subject_race to race, and subject_sex to
sex, but keep the columns in their original order
- Exclude the department_id and department_name columns
Hint:
- Remember that you can chain dplyr commands together with
%>%.*
- You don’t need to save the results. If you save the changes, use a
different name such as police_something.
Matching names
We can also select by matching patterns in the names of the columns.
The patterns to match are in quotes because they aren’t column names –
just character data.
police %>% select(starts_with("contraband"))
police %>% select(ends_with("issued"))
police %>% select(contains("vehicle"))
We can also put a - in front of these helper functions
to exclude columns:
police %>% select(-contains("subject"))
And there are even more select
helper functions.
EXERCISE 4
Use select() to get a copy of police
without the columns that start with “raw”.
Hint: If you mess up your police dataset, re-run the
cell near the top of the file under the Data header and read the data in
again fresh.
Selecting with Vectors or Functions
What if we have the names of the columns we want to select in a
vector already? For example:
analysis_vars <- c("search_basis", "reason_for_stop")
Perhaps we built this vector programatically (we wrote code to
determine the values, instead of typing them ourselves), so we can’t
just rewrite it to:
police %>% select(search_basis, reason_for_stop)
If we just give the vector to select, it looks like we
expect “analysis_vars” to be a column name in police. We get a
warning:
police %>% select(analysis_vars)
This warning tells us what we should do instead, which is use
all_of:
police %>% select(all_of(analysis_vars))
This makes it clearer that “analysis_vars” isn’t the name of a column
in police.
What if we want to select columns of a certain type – for example,
only the numeric columns?
police %>% select(where(is.numeric))
is.numeric is the name of a function. We just use the
name without (). This function is applied to each column,
and if it returns TRUE, then the column is selected. Like above with
using a vector, we wrap the function we want to use in
where to make it clear that we’re using a function, not
looking for a column named “is.numeric”).
where can be used with any function that returns a
single TRUE or FALSE value for each column.
Exercise
Select only columns with character data that contain ‘raw’ in their
name.
Filter: Choose Rows
The filter() function lets us choose which rows of data
to keep by writing expressions that return TRUE or FALSE for every row
in the data frame. Recall from last session:
police %>% filter(date == "2017-01-02")
filter(police, date == "2017-01-02")
We can do complex conditions as we could do with []
police %>% filter(subject_race == "hispanic" & subject_sex == "female")
If we include multiple comma-separated conditions, they are joined
with & (and). So this following is equivalent to the
above.
police %>% filter(subject_race == "hispanic", subject_sex == "female")
EXERCISES
- Filter
police to choose the rows where location is
60201 or 60202
- Filter
police to choose the rows where location is
60201 or 60202, and subject_sex is “male”
Hints:
- The “or” operator is
|; the “and” operator is
&
Bonus: slice variants
Last session, we saw slice() briefly as a way to choose
which rows we want by their integer index value. But, there are some
useful variants on the slice function that help us select
rows that have the maximum or minimum value of a particular
variable:
mtcars %>% slice_max(hp)
slice_max(mtcars, hp)
By default it just gives us one row, but we can ask for more than one
by setting the n argument:
slice_max(mtcars, hp, n=3)
We got 4 rows above because there was a tie at position 3. There’s an
option with_ties that can change how ties are handled.
There’s also a minimum version:
slice_min(mtcars, disp)
Like we did with slice_max, can also specify the
n argument.
Mutate: Change or Create Columns
mutate() is used to both change the values of an
existing column and make a new column.
We name the column we’re mutating and set the value. If the name
already exists, it will update the column. If the name doesn’t exist, it
will create a new variable (column is appended at the end of existing
columns).
police %>% mutate(vehicle_age = 2017 - vehicle_year) %>%
select(starts_with("vehicle")) # just to pick a few columns to look at
We can put multiple mutations in the same call to mutate, with the
expressions separated by commas:
mutate(police,
vehicle_age = 2017 - vehicle_year,
old_car = vehicle_year < 2000)
Within a call to mutate, we can refer to variables we made or changed
earlier in the same call as well. Here, we create vehicle_age, and then
use it to create vehicle_age_norm:
police %>%
mutate(vehicle_age = 2017 - vehicle_year,
vehicle_age_norm = ifelse(vehicle_age < 0, # ifelse test condition
0, # value if true
vehicle_age) # value if false
) %>%
# below is just making it easier for us to see what we changed
select(starts_with("vehicle")) %>%
filter(vehicle_age < 0)
Side note: there is a tidyverse version of ifelse()
called if_else() – with the underscore or low dash symbol –
that works generally the same except it is stricter about checking data
types.
mutate() can also change an existing column. The
location column in the data contains zip codes, that were read in as
numeric values. This means the leading zero on some zip codes has been
lost. Convert location to character data, and add back in the leading 0
if it should be there.
Here I’ll change the location column twice in the same call with two
different transformations:
police %>%
mutate(location = as.character(location), # first convert to character, then recode below
location = ifelse(nchar(location) == 4, # ifelse test (vector of TRUE and FALSE)
paste0("0", location), # value if TRUE
location)) %>% # value if FALSE
select(location) %>% # selecting just the column we mutated to look at
filter(startsWith(location, "0")) # selecting a few rows to look at the change
Remember that when using mutate(), you’re operating on
the entire column at once, so you can’t select just a subset of the
vector as you would with []. This means more frequently
using functions like ifelse() or helper functions such as
na_if(), replace_na(), or
recode().
na_if replaces an existing value with NA.
replace_na does roughly the opposite: replaces
NA with a new value.
police %>% mutate(vehicle_make = na_if(vehicle_make, "UNK"))
na_if() can only can check and replace one value at a
time; it also can’t be used with any expressions
(x <= 1) – only single values.
EXERCISE
If the column beat in police is “/” or “CHICAGO”, set it
to NA instead using mutate().
Hint: it’s ok if you take two steps to do this.
Recap
You now can use select and filter to subset
your data in a wide variety of ways, and mutate to update
variables or create new ones.
Next session: the three other common dplyr “verb” functions for
working with data frames: group_by, summarize,
and arrange.
---
title: 'dplyr: select, filter, mutate'
output:
  html_document:
    df_print: paged
    code_download: TRUE
    toc: true
    toc_depth: 2
editor_options:
  chunk_output_type: inline
---

```{r, setup, include=FALSE}
# you don't need to run this when working in RStudio
knitr::opts_chunk$set(eval=FALSE)  # when making the html version of this file, don't execute the code
```

This is an [R Markdown](https://rmarkdown.rstudio.com/) document.  Follow the link to learn more about R Markdown and the notebook format used during the workshop.

# Setup

```{r, eval=TRUE}
library(tidyverse)
```


The data is from the [Stanford Open Policing Project](https://openpolicing.stanford.edu/data/) and includes vehicle stops by the Evanston police in 2017.  We're reading the data in from a URL directly.  

```{r, eval=TRUE}
police <- read_csv("https://raw.githubusercontent.com/nuitrcs/r-tidyverse/main/data/ev_police.csv",
                   col_types=c( "location"="c"))
```


# dplyr

dplyr is at the core of the tidyverse.  It contains six main functions, each a verb, of actions you frequently take with a data frame.  We covered a few of these in the intro and will build on select, filter, and mutate in this document.

If you have a background in Excel, you probably will start thinking about equivalences in your mind. That's great! If you have worked on base R (not tidyverse), don't overthink it. What's the most important thing to keep in mind right now? Each of these functions takes a data frame as the first input.  Within the function call, we can refer to the column names without quotes and without $ notation. 

We will begin with the `select` for choosing columns, and `filter` for choosing rows. Remember `select` is for columns, while `filter` is for rows. Let's start!

# Select: Choose Columns

The previous session covered the basics of `select` but there are many more options for how we can specify which columns to choose.

First, let's remember what the column names are:

```{r, eval=F}
names(police)
```

Recall, the `select` function takes as the first input a data frame, and then we can list one or more columns, names unquoted, that we want to select. The columns will be ordered in the order we specify them. Notice that the order in the output is different than the original order above.

```{r, eval=F}
police %>% select(outcome, date)
select(police, outcome, date)
```


## Ranges

There are a number of select helper functions and special syntax options that allow us to choose multiple columns.

First, we can use : for range, but with names in addition to numbers:

```{r, eval=F}
police %>% select(raw_DriverRace:raw_ResultOfStop)
```

The result is the same if we use numbers.

```{r, eval=F}
police %>% select(26:29)
```

We can select the rightmost columns with `last_col()`. You can check about with `names` that it is the "last" column in our dataset.

```{r, eval=F}
police %>% select(last_col())
```

You can think `everything()` as the opposite of selecting one or a few columns. But it is most useful when combined with other functions. After all, we use select because you don't want the whole database but part of it! We will see the use of `everything()` and other selection operators below.  

```{r, eval=F}
police %>% select(everything())
```


## Excluding columns

We can also say which columns we don't want by putting a `-` (minus) in front of the name. Think about it as subtracting or or more columns. The idea of subtracting makes a lot of sense -- we have now 27 rather than the original 29 columns in the data. 

```{r, eval=F}
police %>% select(-raw_row_number, -subject_age)
```

When using negated `-` column names, if we start with negations, it will get messy. Make sure to *avoid* the following code:

```{r, eval=F}
police %>% select(-raw_row_number, -subject_age, time:outcome) 
```

To both specify the columns wanted and exclude some that would otherwise be selected, the exclusions need to come at the end:

```{r, eval=F}
police %>% select(time:outcome, -subject_age) 
```

We don't need to include -raw_row_number -- it is not part of the range between time and outcome. If we do include it, we will still get the same result.  

```{r, eval=F}
police %>% select(time:outcome, -subject_age, -raw_row_number) 
```

## Reordering and renaming

We've already seen that columns will appear in the result in the order that we list the names.  

If you want to pull a few columns over to the left so that they are the ones that show first when you look at the data you can use the everything() helper function. 

We did this as well in the intro, see if you can write code below to use select() and everything() to bring outcome to the left from police. 

## Exercise:

```{r, eval=F}

```

Each column only gets included once, in the position that it first appears.  So "outcome" becomes the leftmost column above and no longer appears in it's original spot.  

We can also rename columns while using `select()`.  The syntax is `new_name = old_name`.

```{r, eval=F}
police %>% select(raw_id=raw_row_number, date, time)
```

or we can use `rename()` to only rename, without affecting which columns are included or their order (all of the columns are kept in the same order):

```{r, reval=F}
police %>% rename(raw_id=raw_row_number)
```

Remember, this doesn't change police because we didn't save the result.  So far, we've just been printing the copy of the data frame that is returned by the function.  If we want to change our data frame, we'd need to save the result back to the `police` object.We can also save the changes in another object with a different name such as `police_new`.

## Exercise: 

Assign the results of renaming raw_row_number to raw_id in police to police_new

```{r, eval=F}
police_new <- 
```

We saved the same data with a different name for one column, while keep all other and their original order. But we can also save only a few variables. For convenience, we  name `police_short` our new object.

```{r, eval=F}
police_short <- select(police, raw_row_number, date, time)
```


### EXERCISES

Remember: run the cells above to load tidyverse and import the data.

Using `select` and/or `rename` as needed:

- Rename subject_age to age, subject_race to race, and subject_sex to sex, but keep the columns in their original order
- Exclude the department_id and department_name columns

Hint: 

- Remember that you can chain dplyr commands together with `%>%`.*
- You don't need to save the results. If you save the changes, use a different name such as *police_something*.


```{r}

```



## Matching names


We can also select by matching patterns in the names of the columns.  The patterns to match are in quotes because they aren't column names -- just character data.

```{r, eval=F}
police %>% select(starts_with("contraband"))
```

```{r, eval=F}
police %>% select(ends_with("issued"))
```

```{r, eval=F}
police %>% select(contains("vehicle"))
```

We can also put a `-` in front of these helper functions to exclude columns:

```{r, eval=F}
police %>% select(-contains("subject"))
```


And there are even more [select helper functions](https://tidyselect.r-lib.org/reference/language.html).  

### EXERCISE 4

Use `select()` to get a copy of `police` without the columns that start with "raw".

Hint: If you mess up your `police` dataset, re-run the cell near the top of the file under the Data header and read the data in again fresh.


```{r, }

```


## Selecting with Vectors or Functions

What if we have the names of the columns we want to select in a vector already?  For example:

```{r, eval=TRUE}
analysis_vars <- c("search_basis", "reason_for_stop")
```

Perhaps we built this vector programatically (we wrote code to determine the values, instead of typing them ourselves), so we can't just rewrite it to: 

```{r, eval=F}
police %>% select(search_basis, reason_for_stop)
```

If we just give the vector to `select`, it looks like we expect "analysis_vars" to be a column name in police.  We get a warning:

```{r}
police %>% select(analysis_vars)
```

This warning tells us what we should do instead, which is use `all_of`:

```{r, eval=F}
police %>% select(all_of(analysis_vars))
```

This makes it clearer that "analysis_vars" isn't the name of a column in police.

What if we want to select columns of a certain type -- for example, only the numeric columns?  

```{r, eval=F}
police %>% select(where(is.numeric))
```

`is.numeric` is the name of a function.  We just use the name without `()`.  This function is applied to each column, and if it returns TRUE, then the column is selected.  Like above with using a vector, we wrap the function we want to use in `where` to make it clear that we're using a function, not looking for a column named "is.numeric").  

`where` can be used with any function that returns a *single* TRUE or FALSE value for each column.  

## Exercise 

Select only columns with character data that contain 'raw' in their name.

```{r}

```


# Filter: Choose Rows

The `filter()` function lets us choose which rows of data to keep by writing expressions that return TRUE or FALSE for every row in the data frame.  Recall from last session:

```{r, eval=F}
police %>%  filter(date == "2017-01-02")
filter(police, date == "2017-01-02")
```

We can do complex conditions as we could do with `[]`

```{r, eval=F}
police %>% filter(subject_race == "hispanic" & subject_sex == "female")
```

If we include multiple comma-separated conditions, they are joined with `&` (and).  So this following is equivalent to the above.

```{r, eval=F}
police %>% filter(subject_race == "hispanic", subject_sex == "female")
```


### EXERCISES

1. Filter `police` to choose the rows where location is 60201 or 60202
2. Filter `police` to choose the rows where location is 60201 or 60202, and subject_sex is "male"

Hints:

* The "or" operator is `|`; the "and" operator is `&`

```{r}

```

## Including Variable Transformations

When filtering, we can include transformations of variables in our expressions.  To see this, we'll use the built-in `mtcars` dataset.

Here's what `mtcars` looks like:

```{r}
mtcars
```

Now, let's filter to see which cars have above average mpg:

```{r, eval=F}
mtcars %>% filter(mpg > mean(mpg))
```

Or which car has the most horsepower (hp):

```{r,  eval=F}
mtcars %>% filter(hp == max(hp))
```


### EXERCISE

Using `mtcars`, find the car with the minimum (`min`) displacement (disp) value:

```{r}

```


# Bonus: slice variants

Last session, we saw `slice()` briefly as a way to choose which rows we want by their integer index value.  But, there are some useful variants on the `slice` function that help us select rows that have the maximum or minimum value of a particular variable:

```{r, eval=TRUE}
mtcars %>% slice_max(hp)
slice_max(mtcars, hp)
```

By default it just gives us one row, but we can ask for more than one by setting the `n` argument:

```{r, eval=TRUE}
slice_max(mtcars, hp, n=3)
```

We got 4 rows above because there was a tie at position 3.  There's an option `with_ties` that can change how ties are handled.  

There's also a minimum version:

```{r, eval=TRUE}
slice_min(mtcars, disp)
```

Like we did with `slice_max`, can also specify the `n` argument.

# Mutate: Change or Create Columns

`mutate()` is used to both change the values of an existing column and make a new column.  

We name the column we're mutating and set the value.  If the name already exists, it will update the column. If the name doesn't exist, it will create a new variable (column is appended at the end of existing columns).  

```{r, eval=TRUE}
police %>% mutate(vehicle_age = 2017 - vehicle_year) %>%
  select(starts_with("vehicle"))  # just to pick a few columns to look at
```

We can put multiple mutations in the same call to mutate, with the expressions separated by commas:

```{r,  eval=TRUE}
mutate(police, 
       vehicle_age = 2017 - vehicle_year,
       old_car = vehicle_year < 2000)
```


Within a call to mutate, we can refer to variables we made or changed earlier in the same call as well.  Here, we create vehicle_age, and then use it to create vehicle_age_norm:

```{r, eval=TRUE}
police %>% 
  mutate(vehicle_age = 2017 - vehicle_year, 
         vehicle_age_norm = ifelse(vehicle_age < 0,  # ifelse test condition
                                   0,  # value if true
                                   vehicle_age)  # value if false
         ) %>%  
  # below is just making it easier for us to see what we changed
  select(starts_with("vehicle")) %>%
  filter(vehicle_age < 0)
```

Side note: there is a tidyverse version of `ifelse()` called `if_else()` -- with the underscore or low dash symbol -- that works generally the same except it is stricter about checking data types.

`mutate()` can also change an existing column.  The location column in the data contains zip codes, that were read in as numeric values.  This means the leading zero on some zip codes has been lost.  Convert location to character data, and add back in the leading 0 if it should be there.

Here I'll change the location column twice in the same call with two different transformations:

```{r, eval=TRUE}
police %>%
  mutate(location = as.character(location),  # first convert to character, then recode below
         location = ifelse(nchar(location) == 4,  # ifelse test (vector of TRUE and FALSE)
                           paste0("0", location), # value if TRUE
                           location)) %>%  # value if FALSE
  select(location) %>%  # selecting just the column we mutated to look at
  filter(startsWith(location, "0"))  # selecting a few rows to look at the change
```

Remember that when using `mutate()`, you're operating on the entire column at once, so you can't select just a subset of the vector as you would with `[]`.  This means more frequently using functions like `ifelse()` or helper functions such as `na_if()`, `replace_na()`, or `recode()`.  

`na_if` replaces an existing value with `NA`.  `replace_na` does roughly the opposite: replaces `NA` with a new value.

```{r}
police %>% mutate(vehicle_make = na_if(vehicle_make, "UNK"))
```

`na_if()` can only can check and replace one value at a time; it also can't be used with any expressions (`x <= 1`) -- only single values.


### EXERCISE

If the column beat in `police` is "/" or "CHICAGO", set it to `NA` instead using `mutate()`.  

Hint: it's ok if you take two steps to do this.    

```{r}
```



# Recap

You now can use `select` and `filter` to subset your data in a wide variety of ways, and `mutate` to update variables or create new ones.  

Next session: the three other common dplyr "verb" functions for working with data frames: `group_by`, `summarize`, and `arrange`.  

